A lot of research has been done to address the privacy-utility trade-off in the local model of privacy. In standard LDP, typically the focus is on optimizing the utility from the point of view of the data collector, i.e., devising mechanisms and post-processing methods that would allow deriving maximally accurate statistics from the collection of the noisy data [1]}, [2]}. Statistical precision is essential for various kinds of studies and analytics performed by data consumers, for reasons including and not limited to improving and upgrading the standard of service, providing useful statistics to companies and research institutions, training machine learning models [2]}, [4]}, etc.
